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Computer Science > Computational Engineering, Finance, and Science

arXiv:1309.7695v1 (cs)
[Submitted on 30 Sep 2013]

Title:GPU-powered Simulation Methodologies for Biological Systems

Authors:Daniela Besozzi (University of Milano), Giulio Caravagna (University of Milano Bicocca), Paolo Cazzaniga (University of Bergamo), Marco Nobile (University of Milano Bicocca), Dario Pescini (University of Milano Bicocca), Alessandro Re (University of Milano Bicocca)
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Abstract:The study of biological systems witnessed a pervasive cross-fertilization between experimental investigation and computational methods. This gave rise to the development of new methodologies, able to tackle the complexity of biological systems in a quantitative manner. Computer algorithms allow to faithfully reproduce the dynamics of the corresponding biological system, and, at the price of a large number of simulations, it is possible to extensively investigate the system functioning across a wide spectrum of natural conditions. To enable multiple analysis in parallel, using cheap, diffused and highly efficient multi-core devices we developed GPU-powered simulation algorithms for stochastic, deterministic and hybrid modeling approaches, so that also users with no knowledge of GPUs hardware and programming can easily access the computing power of graphics engines.
Comments: In Proceedings Wivace 2013, arXiv:1309.7122
Subjects: Computational Engineering, Finance, and Science (cs.CE); Distributed, Parallel, and Cluster Computing (cs.DC)
Cite as: arXiv:1309.7695 [cs.CE]
  (or arXiv:1309.7695v1 [cs.CE] for this version)
  https://doi.org/10.48550/arXiv.1309.7695
Journal reference: EPTCS 130, 2013, pp. 87-91
Related DOI: https://doi.org/10.4204/EPTCS.130.14

Submission history

From: EPTCS [view email] [via EPTCS proxy]
[v1] Mon, 30 Sep 2013 01:06:39 UTC (10 KB)
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